To examine the possibility of a data augmentation technique, we also consider super-resolution analysis [1]}.
We here examine two methods of training, global data and local data, as shown in figure REF \((c)\) .
We utilize the same network structure for both training processes, which is an up-sampling-based CNN.
In the model, the input low-resolution data is extended up to the dimension in which it matches with the data size of high-resolution data, and then the data are convolved to output section.
